A method for improving the throughput of bacterial colony selection

Abstract

The invention provides a colony selecting throughout improving method, which is applied to a colony selector so as to shorten a selecting path of a probe module and to subsequently improve a selecting throughout; the colony selecting throughout improving method is characterized by comprising the following steps: 1, establishing a mathematical model of the selecting path; 2, sequentially distributing colony numbers and probe numbers, so that an initial chromosome is obtained, and obtaining an initial population in a mode of random insertion; 3, conducting an iterative operation; 4, repeating the step 3 until iteration times reach preset times, so that a final population is obtained; and 5, selecting a chromosome, which has the maximum fitted value, from the final population and outputting the selected chromosome as a result, so that the colony selector can conduct colony selecting in accordance with colony and probe matching in the result. With the application of the colony selecting throughout improving method provided by the invention, the probe module of the colony selector can be optimized, so that the moving path of the probe module is shortened in a selecting course and subsequently the colony selecting throughout is improved.

Description

technical field [0001] The invention relates to a method for improving the throughput of colony selection based on an improved genetic algorithm. Background technique [0002] The colony sorting instrument is an intelligent instrument that has developed rapidly in the field of biotechnology in recent years. It has the functions of automatic identification and selection of colonies, and is one of the basic instruments for realizing the automation of colony screening. [0003] In this type of colony picking instrument, multiple probes are integrated on the probe module, and the selection process can be summarized as follows: the control module controls the movement of the probe module so that one of the probes is facing a colony to be selected, and the After the probe is selected, the probe module is controlled to move and fine-tune, so that the other probe is facing the next colony to be selected and selected, and so on, all the colonies to be selected are selected one by one...

AI Technical Summary

Problems solved by technology

However, when traditional genetic algorithms face practical problems, their coding operations are usually difficult

Moreover, the genetic algorithm is more sensitive to the quality of the initial population; the traditional crossover operation has poor adaptability to the problem, making the convergence speed slower; the traditional mutation operation is easy to destroy excellent chromosomes, or it is difficult to make the chromosome population reach a sufficient degree of variation; the selection algorithm Improper sub-setting can easily cause the population to fall into a local solution

[0008] Due to the above-mentioned various defects, even if the genetic algorithm is directly applied to the improvement of the colony picker, it is difficult to achieve the purpose of greatly shortening the probe module path and increasing the picking throughput

Images